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Marine Atmospheric Boundary Layer Profiles via Satellite-based Remote Sensing Data Fusion

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-22-C-0298
Agency Tracking Number: N22A-T024-0162
Amount: $246,416.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N22A-T024
Solicitation Number: 22.A
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-06-06
Award End Date (Contract End Date): 2023-12-11
Small Business Information
9301 Corbin Avenue Suite 2000
Northridge, CA 91324-1111
United States
DUNS: 082191198
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nicholas Elmer
 (256) 203-6429
Business Contact
 Greg Fetzer
Phone: (303) 651-6756
Research Institution
 The University of Alabama
 John Mecikalski
739 University Blvd
Tuscaloosa, AL 35487-0000
United States

 (256) 961-7046
 Nonprofit College or University

Satellite-based atmospheric soundings are extremely valuable in remote regions lacking nearby radiosondes. They provide a depiction of temperature and water vapor structure to improve prediction of clouds, storms, and rainfall, and massively contribute to numerical weather prediction (NWP) via data assimilation. However, satellite profiles are too smooth and under-determined, thereby suffering from large biases, low vertical resolution, and other random errors, making them less useful for depicting important phenomena in the marine atmospheric boundary layer (MABL) that can aid weather forecasters and at-sea Naval operations. To address the need to improve at-sea Naval operations, the University of Alabama in Huntsville (UAH) and Areté propose the development of an artificial intelligence (AI) framework that performs data fusion of single-source satellite profile retrievals from multiple modalities and sensors, NWP forecasts, and satellite-derived sea surface temperature and winds to produce temperature and water vapor profiles of the MABL at 100 m vertical resolution. Phase I technical objectives include: 1) a regionalized proof-of-concept demonstration in deriving 100 m vertical resolution thermodynamic profiles within the MABL using AI, 2) evaluating single-source profile retrieval biases and overall importance in contributing to enhanced understanding of the MABL, and 3) benchmarking AI profiles against state-of-the-art profile retrievals.

* Information listed above is at the time of submission. *

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